Error when using dplyr inside of a function

If quosure and quasiquotation are too much for you, use either .data[[ ]] or rlang {{ }} (curly curly) instead. See Hadley Wickham's 5min video on tidy evaluation and (maybe) Tidy evaluation section in Hadley's Advanced R book for more information.

library(rlang)
library(dplyr)

filter_data <- function(df, spp, LENGTH, WIDTH) {
  res <- df %>% 
    filter(Species == spp) %>% 
    select(.data[[LENGTH]], .data[[WIDTH]]) %>%        
    mutate(sum = .data[[LENGTH]] + .data[[WIDTH]]) %>% 
    filter(sum > 4) %>% 
    nrow()
  return(res)
}

filter_data(iris, "virginica", "Sepal.Length", "Sepal.Width")
#> [1] 50


filter_rlang <- function(df, spp, LENGTH, WIDTH) {
  res <- df %>% 
    filter(Species == spp) %>% 
    select({{LENGTH}}, {{WIDTH}}) %>%        
    mutate(sum = {{LENGTH}} + {{WIDTH}}) %>% 
    filter(sum > 4) %>% 
    nrow()
  return(res)
}

filter_rlang(iris, "virginica", Sepal.Length, Sepal.Width)
#> [1] 50

^{Created on 2019-11-10 by the reprex package (v0.3.0)}

You are running into NSE/SE problems, see the vignette for more info.

Briefly, dplyr uses a non standard evaluation (NSE) of names, and passing names of columns into functions breaks it, without using the standard evaluation (SE) version.

The SE versions of the dplyr functions end in _. You can see that select_ works nicely with your original arguments.

However, things get more complicated when using functions. We can use lazyeval::interp to convert most function arguments into column names, see the conversion of the mutate to mutate_ call in your function below and more generally, the help: ?lazyeval::interp

Try:

filter <- function (spp, LENGTH, WIDTH) {
    d <- subset (iris, subset=iris$Species == spp) 
    large <- d %>%                       
        select_(LENGTH, WIDTH) %>%  
        mutate_(sum = lazyeval::interp(~X + Y, X = as.name(LENGTH), Y = as.name(WIDTH))) 
    big_samples <- which(large$sum > 4)
    return (length(big_samples)) 
}

UPDATE: As of dplyr 0.7.0 you can use tidy eval to accomplish this.

See http://dplyr.tidyverse.org/articles/programming.html for more details.

filter_big <- function(spp, LENGTH, WIDTH) {
  LENGTH <- enquo(LENGTH)                    # Create quosure
  WIDTH  <- enquo(WIDTH)                     # Create quosure

  iris %>% 
    filter(Species == spp) %>% 
    select(!!LENGTH, !!WIDTH) %>%            # Use !! to unquote the quosure
    mutate(sum = (!!LENGTH) + (!!WIDTH)) %>% # Use !! to unquote the quosure
    filter(sum > 4) %>% 
    nrow()
}

filter_big("virginica", Sepal.Length, Sepal.Width)

> filter_big("virginica", Sepal.Length, Sepal.Width)
[1] 50